Breast Imaging Reporting and Data System (BI‐RADS) breast composition descriptors: Automated measurement development for full field digital mammography. Issue 11 (21st October 2013)

• Record Type: Journal Article
• Authors: Fowler, E. E.; Sellers, T. A.; Lu, B.; Heine, J. J.
• Is Part Of: Medical physics. Volume 40:Issue 11(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-10-21
• Subjects: Mammography -- Digital mammography -- Cancer; biomedical engineering -- cancer -- density -- mammography; mammography -- breast density -- BI‐RADS -- calibration -- differential evolution optimization; Biological material, e.g. blood, urine; Haemocytometers; Medical imaging -- Density measurement -- Optimization -- Calibration -- Cancer -- Mammography -- Medical X‐ray imaging -- Biomedical modeling -- Film mammography -- Tissues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4824319
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The Breast Imaging Reporting and Data System (BI‐RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI‐RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data. Methods: : A matched case‐control dataset with FFDM images was used to develop three automated measures for the BI‐RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BRpg measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BRvc and BRvr measures, respectively. These three measures were compared with the radiologist‐reported BI‐RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method‐1 used breast cancer status; and method‐2 matched the reported BI‐RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measureˈs association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals. Results: : The three BI‐RADS measures generated by method‐1 had κ between 0.25–0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BRpg ; (b) OR = 1.93 (1.36, 2.74) for BRvc ; and (c) OR = 1.37 (1.05, 1.80) for BRvr . The measures generated by method‐2 had κ between 0.42–0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BRpg ; (b) OR = 1.42 (0.87, 2.32) for BRvc ; and (c) OR = 2.13 (1.22, 3.72) for BRvr . The radiologist‐reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant. Conclusions: : A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI‐RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.